能動推論と精度学習による確率的フォールトトレラント制御
Towards Stochastic Fault-tolerant Control using Precision Learning and Active Inference
ロボットマニピュレータのセンサ故障に対し、閾値を使わずセンサ精度をオンライン学習して徐々に故障センサを除外する能動推論ベースの確率的フォールトトレラント制御を提案し、実験で有効性を示した。
著者: Mohamed Baioumy, Corrado Pezzato, Carlos Hernandez Corbato, Nick Hawes, Riccardo Ferrari
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
This work presents a fault-tolerant control scheme for sensory faults in robotic manipulators based on active inference. In the majority of existing schemes, a binary decision of whether a sensor is healthy (functional) or faulty is made based on measured data. The decision boundary is called a threshold and it is usually deterministic. Following a faulty decision, fault recovery is obtained by excluding the malfunctioning sensor. We propose a stochastic fault-tolerant scheme based on active inference and precision learning which does not require a priori threshold definitions to trigger fault recovery. Instead, the sensor precision, which represents its health status, is learned online in a model-free way allowing the system to gradually, and not abruptly exclude a failing unit. Experiments on a robotic manipulator show promising results and directions for future work are discussed.
